Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices,Energy & Environmental Science

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Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices
Energy & Environmental Science ( IF 32.4 ) Pub Date : 2022-10-21 , DOI: 10.1039/d2ee01844f
Mortaza Saeidi-Javash _{1,

2} , Ke Wang ₃ , Minxiang Zeng _{1,

4} , Tengfei Luo _{1,

3} , Alexander W. Dowling ₃ , Yanliang Zhang ₁

Affiliation

Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a power source for wearable electronics and the Internet of Things. A key challenge preventing large-scale application of TEGs lies in the lack of a high-throughput processing method, which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties. Herein, we integrate high-throughput experimentation and Bayesian optimization (BO) to accelerate the discovery of the optimum sintering conditions of silver–selenide TE films using an ultrafast intense pulsed light (flash) sintering technique. Due to the nature of the high-dimensional optimization problem of flash sintering processes, a Gaussian process regression (GPR) machine learning model is established to rapidly recommend the optimum flash sintering variables based on Bayesian expected improvement. For the first time, an ultrahigh-power factor flexible TE film (a power factor of 2205 μW m⁻¹ K⁻² with a zT of 1.1 at 300 K) is demonstrated with a sintering time less than 1.0 second, which is several orders of magnitude shorter than that of conventional thermal sintering techniques. The films also show excellent flexibility with 92% retention of the power factor (PF) after 10³ bending cycles with a 5 mm bending radius. In addition, a wearable thermoelectric generator based on the flash-sintered films generates a very competitive power density of 0.5 mW cm⁻² at a temperature difference of 10 K. This work not only shows the tremendous potential of high-performance and flexible silver–selenide TEGs but also demonstrates a machine learning-assisted flash sintering strategy that could be used for ultrafast, high-throughput and scalable processing of functional materials for a broad range of energy and electronic applications.

中文翻译：

机器学习辅助的高性能和柔性硒化银热电器件的超快闪光烧结

柔性热电发电机 (TEG) 已显示出作为可穿戴电子设备和物联网电源的巨大潜力。阻碍 TEG 大规模应用的一个关键挑战在于缺乏一种高通量处理方法，该方法可以快速烧结热电 (TE) 材料，同时保持其高热电性能。在这里，我们将高通量实验和贝叶斯优化 (BO) 相结合，使用超快强脉冲光 (闪光) 烧结技术加速发现硒化银 TE 薄膜的最佳烧结条件。由于闪光烧结工艺的高维优化问题的性质，建立了高斯过程回归（GPR）机器学习模型，以根据贝叶斯预期改进快速推荐最佳快速烧结变量。首次研制出超高功率因数柔性 TE 薄膜（功率因数为 2205 μW·m^-1 K^-2 (在 300 K时zT为 1.1) 的烧结时间小于 1.0 秒，比传统的热烧结技术短几个数量级。^{在弯曲半径为 5 mm 的 10 3}次弯曲循环后，这些薄膜还显示出优异的柔韧性，功率因数 (PF) 保持率为 92%此外，基于闪光烧结薄膜的可穿戴热电发电机可产生 0.5 mW cm^{-2的极具竞争力的功率密度}在 10 K 的温差下。这项工作不仅展示了高性能和灵活的硒化银 TEG 的巨大潜力，而且展示了一种机器学习辅助的快速烧结策略，可用于超快、高通量和可扩展的处理用于广泛的能源和电子应用的功能材料。

更新日期：2022-10-21

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